[Prof. Sung-Ju Lee, Jaemin Shin (KAIST PhD Candidate), Prof. Yunxin Liu of Tsinghua University , Prof. Yuanchun Li of Tsinghua University, from left]
The research team led by Prof. Sung-Ju Lee of KAIST has published a paper “FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients” at ACM MobiSys (International Conference on Mobile Systems, Applications, and Services) 2022. Founded in 2003, MobiSys has been a premier conference on Mobile Computing and Systems. This year, 38 out of 176 submitted papers have been accepted to be presented at the conference.
Jaemin Shin (KAIST PhD Candidate) was the lead author, and this work was in collaboration with Tsinghua University in China (Professors Yuanchun Li and Yunxin Liu participated).
Federated Learning is a recent machine learning paradigm proposed by Google that trains on a large corpus of private user data without collecting them. The authors developed a systematic federated learning framework that accelerates the global learning process of federated learning. The new framework actively measures the contribution of each training sample of clients and selects the optimal samples to optimize the training process. The authors also included an adaptive deadline control scheme with varying training data, and achieved 4.5 times speedup in global learning process without sacrificing the model accuracy.
Prof. Lee stated that “Federated learning is an important technology used by many top companies. With the accelerated training time achieved by this research, it has become even more attractive for real deployments. Moreover, our technology has shown to work well in different domains such as computer vision, natural language processing, and human activity recognition.”